Currently, with the rapid expansion of the World Wide Web and integrated sensors, there is more information available than is possible for the human mind to comprehend. As a result, systems and methods are needed that can automatically process data to synthesize new insights and create knowledge.
Systems and methods for creating knowledge include a method for representing knowledge and an inference engine that processes current knowledge into new knowledge. A number of different approaches have been exploited in an attempt to develop systems and methods for representing knowledge. One such method utilizes a semantic network. A semantic network is a structure for encoding knowledge. It can encode knowledge about any domain, such as military operations, financial transactions, organizational relationships, medical conditions and their treatment, to name a few.
A semantic network provides an intuitive way to encode knowledge since it mimics language. The basic building blocks of a semantic network are nodes and links. Nodes typically represent objects, real or conceptual. Links represent relationships between the objects. Nodes and links together constitute total knowledge. Referring to FIG. 1, in one example, a semantic network 80 includes nodes 81 and links 82. Nodes 81 represent concepts, such as a “Person”, “Bank”, or a “Bank Account”. Links represent relationships between nodes, such as “owns account” 82. The combination of nodes 81 and links 82, such as “Person: A” “owns account” “Bank Account: B, encodes domain knowledge, such as A owns bank account B. New nodes and links are added to the semantic network, as new facts become known. In the example of FIG. 1, “Sue” and “Bob” represent real world knowledge about two “Persons” with “Bank Accounts” and “Transactions”. At any time, knowledge can be extracted from the semantic networks through querying for links and nodes. By following certain chains of links, knowledge can be extracted from a semantic network 80. Consider, for example, the darker links 83 in FIG. 2. By following those links, one can conclude that Bob is a Person. In the example in FIG. 3, by following the darker links 84, one can conclude that Bob sends money to Sue.
In these prior art systems, knowledge needs to be explicitly added in the semantic network. New nodes and links are created through an explicit association between data and a node-link pair in the semantic network. As data become available, a computer program implements the explicit association and adds content to the semantic network. In these prior art systems, there is no mechanism for automatically and systematically adding new content to a semantic network, through a process of “inference” applied to the existing content of the semantic network. In addition, prior art semantic networks do not provide mechanisms for representing history. This precludes adding new knowledge through learning changes in the semantic network over time.
Accordingly, there is a need for systems and methods that extend a semantic network to include reasoning which adds new nodes and links to the network over time, as trends are captured or learning occurs or conclusions are drawn, by way of inferences.